Particle Cloud

Using the same setup as detailed_look.jl or example2(), here we simulate a point cloud getting advected by the flow field. For additional documentation e.g. see : 1, 2, 3, 4

1. Import Software

using IndividualDisplacements, OrdinaryDiffEq, Statistics
p=dirname(pathof(IndividualDisplacements))
include(joinpath(p,"../examples/recipes_plots.jl"))
include(joinpath(p,"../examples/example123.jl"));

2. Setup Problem

𝑃,Γ=example2_setup()

ii1=5:5:40; ii2=5:5:25
x=vec([x-0.5 for x in ii1, y in ii2])
y=vec([y-0.5 for x in ii1, y in ii2])
xy=transpose([x y])

𝑃.𝑇[:] = [0.0,2998*3600.0]
solv(prob) = solve(prob,Tsit5(),reltol=1e-6,abstol=1e-6)
tr = DataFrame([fill(Int, 1) ; fill(Float64, 3)], [:ID, :x, :y, :t])

𝐼 = Individuals{Float64}(📌=xy[:,:], 🔴=tr, 🆔=collect(1:size(xy,2)),
                         🚄 = dxy_dt, ∫ = solv, 🔧 = postprocess_xy, 𝑃=𝑃);

3. Compute Trajectories

𝑇=(0.0,𝐼.𝑃.𝑇[2])
∫!(𝐼,𝑇)

2×40 Array{Float64,2}:
   5.91182   10.5953   11.087   12.6622  …  37.9639   29.12    30.6396
 -12.3525   124.028   487.117  478.34        8.68514  19.2392  22.4415

4. Display results

𝐼.🔴.lon=5000*𝐼.🔴.x
𝐼.🔴.lat=5000*𝐼.🔴.y
plt=PlotBasic(𝐼.🔴,size(xy,2),100000.0)

Compare with trajectory output from MITgcm

#df=read_flt(IndividualDisplacements.flt_example,Float32)
#ref=PlotBasic(df,size(xy,2),100000.0)

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